Currently, it is only possible to analyze the flow of activity on individual online social networks, although a single user tends to have accounts in several different online social networks. As a result, an event in one network may have consequences in others, so that the view of larger systems which incorporate many different networks is important. Finding links between these networks would allow one to track activity which flows across the networks, which is not possible with existing methods.
There is a traditional database problem of “entity resolution” in computer science in which the goal is to identify whether two records refer to the same real-world entity. Researchers in the area of entity resolution have devised multiple techniques to correlate two records on multiple grounds, to remove duplicates, as well as aggregate more data of an entity present across databases (see the List of Incorporated Literature References, Literature Reference Nos. 3 and 4). In these references, database records are created and maintained by administrators in a centralized fashion. However, in online social networks, contents and attributes are fully created and maintained by the users themselves. In this case, the accuracy and trustworthiness of the user contents becomes a major factor to be taken into consideration.
Thus, a continuing need exists for a system for aligning user accounts across several online social media platforms to track activity which flows across the social media platforms.